FluentMD: Context-Aware Entity Linking for Finance
FluentMD is our advanced entity-understanding and linking framework, built upon domain-specialized linguistic representations and a context-sensitive alignment mechanism. It normalizes and connects entities across unstructured text, creating a coherent, machine-interpretable knowledge layer for analytics and risk decisions.
Apple reported 14% revenue in Cupertino for the fiscal period ended September 2024, driven by strong sales of the iPhone 15.|
In today's data-driven and LLM-accelerated environment, financial institutions face a rapidly growing stream of high-volume, unstructured textual information. Building a Financial Knowledge Extraction Layer that can interpret, normalize, and connect entities from raw text is foundational for modern analytics and risk workflows.
Domain Mismatch
General-purpose language models miss financial terminology, naming patterns, and disclosure conventions - leading to systematic recognition errors and poor generalization beyond generic sources.
- Lack of domain calibration for sector-specific signals.
- Misreads aliases, abbreviations, and disclosure language.
Fragmented Pipelines
Disconnected NER, coreference, and linking components amplify error propagation and break alignment with enterprise knowledge graphs, blocking truly unified entity understanding.
- Inconsistent outputs across stages hurt downstream quality.
- Hard to integrate with financial knowledge graphs or RAG.
FluentMD builds a domain-specialized, context-adaptive entity alignment layer that keeps financial data coherent from ingestion to downstream analytics.
Our Key Technology
FluentMD is a domain-specialized entity-understanding and linking framework built to normalize, align, and contextualize financial entities across unstructured sources.
Financial Text Flow with FluentMD
Key Features of FluentMD
FluentMD calibrates entity understanding to financial text, keeping mentions coherent from ingestion to downstream analytics.
- Domain-tuned language models for sector terminology and disclosure style.
- Context-aware interpretation of aliases, abbreviations, and temporal cues.
- Unified linking pipeline that keeps outputs stable for analytics and RAG.
Financial text stays opaque, losing critical entities, relationships, and signals.
Brittle rules struggle with alias variation and global naming, adding maintenance overhead and poor scalability.
Domain-specialized representations keep referential coherence, producing normalized entities and triplets ready for knowledge graphs and analytics.
Results Showcase
From earnings calls to playful phrases, FluentMD normalizes and links entities into machine-readable knowledge ready for analytics.
"Apple reported 14% revenue in Cupertino for the fiscal period ended September 2024, driven by strong sales of the iPhone 15."
- Apple Inc. (ORG) - company_id: 28390
- iPhone 15 (PRODUCT)
- 14% revenue (QUANTITY)
- 2024-09-30 (TIME)
- Cupertino (LOCATION) - country_code: US
- Apple Inc. (28390) → reported → 14% revenue (QUANTITY)
- iPhone 15 (PRODUCT) → drove sales for → Apple Inc. (28390)
- Apple Inc. (28390) → reported product sales → iPhone 15 (PRODUCT)
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